Adaptive backstepping control using recurrent neural network forlinear induction motor drive
Faa-Jeng Lin
Rong-Jong Wai
Wen-Der Chou
Shu-Peng Hsu
Dept. of Electr. Eng., Nat. Dong Hwa Univ., Hualien;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb 2002
Volume: 49,
Issue: 1
On page(s): 134-146
ISSN: 0278-0046
References Cited: 28
CODEN: ITIED6
INSPEC Accession Number: 7177971
Digital Object Identifier: 10.1109/41.982257
Current Version Published: 2002-08-07
Abstract
An adaptive backstepping control system using a recurrent neural
network (RNN) is proposed to control the mover position of a linear
induction motor (LIM) drive to compensate the uncertainties including
the friction force in this paper. First, the dynamic model of an
indirect field-oriented LIM drive is derived. Then, a backstepping
approach is proposed to compensate the uncertainties including the
friction force occurred in the motion control system. With the proposed
backstepping control system, the mover position of the LIM drive
possesses the advantages of good transient control performance and
robustness to uncertainties for the tracking of periodic reference
trajectories. Moreover, to further increase the robustness of the LIM
drive, an RNN uncertainty observer is proposed to estimate the required
lumped uncertainty in the backstepping control system. In addition, an
online parameter training methodology, which is derived using the
gradient-descent method, is proposed to increase the learning capability
of the RNN. The effectiveness of the proposed control scheme is verified
by both the simulated and experimental results
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